17 citations · 19 across the 8 of their papers we have counts for
10 papers
SFT-as-Context Mitigates Forgetting in Supervised Fine-Tuning
Kenan Tang, Andong Hua, Chengxuan Qian +2
Supervised fine-tuning (SFT) equips large language models (LLMs) with specialized capabilities, but often comes at the cost of forgetting the general capabilities of their parent m…
From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments
Saket Tiwari, Tejas Kotwal, George Konidaris
We present a novel theoretical framework for deep reinforcement learning (RL) in continuous environments by modeling the problem as a continuous-time stochastic process, drawing on…
Spectral Collapse Drives Loss of Plasticity in Deep Continual Learning
Arjun Prakash, Naicheng He, Kaicheng Guo +5
We investigate why deep neural networks suffer from loss of plasticity in continual learning, and thus fail to learn new tasks without reinitializing parameters. We show that this…
Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces
Saket Tiwari, Omer Gottesman, George Konidaris
Advances in reinforcement learning (RL) have led to its successful application in complex tasks with continuous state and action spaces. Despite these advances in practice, most th…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
On the Geometry of Reinforcement Learning in Continuous State and Action Spaces
Saket Tiwari, Omer Gottesman, George Konidaris
Advances in reinforcement learning have led to its successful application in complex tasks with continuous state and action spaces. Despite these advances in practice, most theoret…
Effects of Data Geometry in Early Deep Learning
Saket Tiwari, George Konidaris
Deep neural networks can approximate functions on different types of data, from images to graphs, with varied underlying structure. This underlying structure can be viewed as the g…
Meta-Learning Parameterized Skills
Haotian Fu, Shangqun Yu, Saket Tiwari +2
We propose a novel parameterized skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space that supports efficient l…
Natural Option Critic
Saket Tiwari, Philip S. Thomas
The recently proposed option-critic architecture Bacon et al. provide a stochastic policy gradient approach to hierarchical reinforcement learning. Specifically, they provide a way…
Hyperbolic Embeddings for Learning Options in Hierarchical Reinforcement Learning
Saket Tiwari, M. Prannoy
Hierarchical reinforcement learning deals with the problem of breaking down large tasks into meaningful sub-tasks. Autonomous discovery of these sub-tasks has remained a challengin…